Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-23T02:36:57.707Z Has data issue: false hasContentIssue false
  • English
  • Français

Land usage classification: a hierarchical neural network approach

Published online by Cambridge University Press:  23 October 2013

E. J. PALOMO ,
D. ELIZONDO  and
G. BRUNSCHWIG
E. J. PALOMO*
Affiliation:
Department of Computer Science, University of Malaga, Malaga, Spain
D. ELIZONDO
Affiliation:
DIGITS, School of Computing and Informatics, Faculty of Technology, De Monfort University, Leicester, UK
G. BRUNSCHWIG
Affiliation:
Clermont Université, VetAgro Sup, UMR1213 Herbivores, BP 10448, F-63000 Clermont-Ferrand, France INRA, UMR1213 Herbivores, Theix, F-63122 Saint-Genès Champanelle, France
*
*To whom all correspondence should be addressed. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

The classification of land usage in mountain grassland bovine areas is important for the management of forage production and grazing in grass-based livestock systems. The present paper proposes a novel, hierarchical neural network-based approach towards the classification of land usage in these areas. A survey of 72 farms was conducted in the Massif Central (France). Information was gathered on geographical characteristics and cutting and/or grazing practices on three general groups of fields: cut only, cut and grazed and grazed only fields. To classify land usage, the data were clustered and visualized in a hierarchical fashion. This was done by using a novel method for the analysis and classification of data based on growing hierarchical self-organizing maps (GHSOM). Self-organizing maps (SOM) have been shown to be successful for the analysis of highly dimensional input data in data mining applications as well as for data visualization. Moreover, the hierarchical architecture of the GHSOM is more flexible than a single SOM in the adaptation process to input data, capturing inherent hierarchical relationships among them. Experimental results show the utility of this approach.

Type
Modelling Animal Systems Research Papers
Information
The Journal of Agricultural Science , Volume 152 , Issue 5 , October 2014 , pp. 817 - 828
Copyright
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Agabriel, C., Sibra, C., Journal, C. & Coulon, J. (2005). Interest and data analysis of survey in livestock farming: reflection over 15 years of practice. Rencontres autour des Recherches sur les Ruminants 12, 331–334.Google Scholar
Agreste (2008). Caractéristiques Générales des Exploitations, France Métropolitaine. Castanet Tolosan, France: Ministère de l'Agriculture, de l'Agroalimentaire et de la Forêt.Google Scholar
Andrieu, N., Josien, E. & Duru, M. (2007). Relationships between diversity of grassland vegetation, field characteristics and land use management practices assessed at the farm level. Agriculture, Ecosystems and Environment 120, 359–369.Google Scholar
Baumont, R., Deux, N., Farruggia, A. & Jouven, M. (2008). Simulation of the sensitivity and adaptation of beef cattle farming systems to extreme climatic events based on permanent pasture. Rencontres autour des Recherches sur les Ruminants 15, 209.Google Scholar
Brunschwig, G., Josien, E. & Bernhard, C. (2006). Contraintes géographiques et modes d'utilisation des parcelles en élevage bovin laitier et allaitant. Fourrages 185, 83–95.Google Scholar
Dittenbach, M., Rauber, A. & Merkl, D. (2001). Recent advances with the growing hierarchical self-organizing Map. In Advances in Self-Organising Maps (WSOM) (Eds Allinson, N., Yin, H., Allinson, L. & Slack, J.), pp. 140–145. London: Springer.Google Scholar
Fleury, P., Dubeuf, B. & Jeannin, B. (1996). Forage management in dairy farms: a methodological approach. Agricultural Systems 52, 199–212.Google Scholar
Fritzke, B. (1995). Growing grid – a self-organizing network with constant neighborhood range and adaptation strength. Neural Processing Letters 2, 9–13.Google Scholar
Garcia-Launay, F., Sibra, C., Molenat, H., Agabriel, C. & Brunschwig, G. (2012). Grassland use in mountain bovine systems according to a hierarchy of geographical determinants. Journal of Agricultural Science, Cambridge 150, 203–217.Google Scholar
Institut de l'Elevage (2008). Les Chiffres Clés 2008: Productions Bovines Lait & Viande. Paris, France: Institut de l'Elevage.Google Scholar
Jain, A. K., Murty, M. N. & Flynn, P. J. (1999). Data clustering: a review. ACM Computing Surveys 31, 264–323.Google Scholar
Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics 43, 59–69.Google Scholar
Martin, G., Hossard, L., Theau, J. P., Therond, O., Josien, E., Cruz, P., Rellier, J. P., Martin-Clouaire, R. & Duru, M. (2009). Characterizing potential flexibility in grassland use. application to the French Aubrac area. Agronomy for Sustainable Development 29, 381–389.Google Scholar
Morlon, P. & Benoit, M. (1990). Etude méthodologique d'un parcellaire d'exploitation agricole en tant que système. Agronomie 10, 499–508.Google Scholar
Mosnier, C., Agabriel, J. & Lherm, M. (2009). Dynamics of suckler cow farms under stochastic crop yields: a recursive discrete stochastic programming approach. In Proceedings of the Conference on Integrated Assessment of Agriculture and Sustainable Development: Setting the Agenda for Science and Policy (AgSAP 2009), Hotel Zuiderduin, Egmond aan Zee, The Netherlands, 10–12 March 2009 (Eds van Itterson, M., Wolfe, J. & van Laar, G.), pp. 264–265. Wageningen, The Netherlands: Wageningen University and Research Centre.Google Scholar
Rauber, A., Merkl, D. & Dittenbach, M. (2002). The growing hierarchical self-organizing map: Exploratory analysis of high-dimensional data. IEEE Transactions on Neural Networks 13, 1331–1341.CrossRefGoogle ScholarPubMed
Teissier, J. H. (1979). Relations entre techniques et pratiques. Bulletin INRAP 38, 1–19.Google Scholar
Thenail, C. & Baudry, J. (2004). Variation of farm spatial land use pattern according to the structure of the hedgerow network (bocage) landscape: a case study in northeast Brittany. Agriculture Ecosystems and Environment 101, 53–72.Google Scholar
Vesanto, J. (1999). SOM-based data visualization methods. Intelligent Data Analysis 3, 111–126.Google Scholar